- Copy content of
vsix-bookmarklet, create a bookmark in your browser. - Navigate to the web page of the VS Code extension you want to install.
- Click the bookmark you just created, then click the download button.

- After download finished, rename the file extension to
*.vsix. - In VS Code, select Install from VSIX... in the extension context menu.

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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| diff --git a/builddefs/common_features.mk b/builddefs/common_features.mk | |
| index 18f8b0bbfc..4ef3e230e4 100644 | |
| --- a/builddefs/common_features.mk | |
| +++ b/builddefs/common_features.mk | |
| @@ -878,6 +878,10 @@ ifeq ($(strip $(JOYSTICK_ENABLE)), yes) | |
| endif | |
| endif | |
| +ifeq ($(strip $(APPLE_FN_ENABLE)), yes) | |
| + OPT_DEFS += -DAPPLE_FN_ENABLE |
| { | |
| "name": "MN dental receptionist speed to lead", | |
| "nodes": [ | |
| { | |
| "parameters": { | |
| "httpMethod": "POST", | |
| "path": "00000000-0000-4000-8000-000000000001", | |
| "responseMode": "responseNode", | |
| "options": {} | |
| }, |
| /* | |
| oh4_lbp_serializer.h - v0.1 - public domain | |
| Authored 2026 by Eric Scrivner | |
| no warranty implied; use at your own risk | |
| Before including, | |
| #define OH4_LBP_SERIALIZER_IMPLEMENTATION | |
| in the file that you want to have the implementation. |
Preamble:
In this post I will explore how to stream a video and audio capture from one computer to another using ffmpeg and netcat, with a latency below 100ms, which is good enough for presentations and general purpose remote display tasks on a local network.
The problem:
Streaming low-latency live content is quite hard, because most software-based video codecs are designed to achieve the best compression and not best latency. This makes sense, because most movies are encoded once and decoded often, so it is a good trade-off to use more time for the encoding than the decoding.
Purpose: A personal LLM-maintained wiki for scientific curiosity, research synthesis, ideas, writing, projects, experiments, decisions, dead ends, and lessons learned.
Pattern: This extends the LLM-wiki pattern: raw sources are not just retrieved at query time; they are incrementally compiled into a persistent, interlinked markdown wiki.
Extension: This adds WIP gravity wells: bounded capsules where related notes, sources, conversations, decisions, drafts, artifacts, and lessons accumulate around a maturing outcome.
LLM role: Maintain the wiki. Read sources, discuss takeaways, create/update pages, keep links current, surface contradictions, update WIP capsules, answer queries, lint the wiki, and log changes. >